Discovering causal genes, brain regions and other risk factors for Alzheimer'a disease

发现阿尔茨海默病的致病基因、大脑区域和其他危险因素

• 批准号: 10116249
• 负责人: Wei Pan
• 金额: $ 62.13万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2025-02-21
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10116249
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Area; Biological; Brain; Brain imaging; Brain region; Clinical; Cohort Studies; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Detection; Disease; Documentation; Early Diagnosis; Environment; Etiology; Gene Expression; Genes; Genetic; Goals; Image; Incidence; Individual; Intervention; Joints; Lasso; Least-Squares Analysis; Life Style; Linkage Disequilibrium; Mediating; Mendelian randomization; Methods; Modeling; Molecular; Observational Study; Observational epidemiology; Outcome; Phenotype; Prevention; Public Domains; Research; Risk; Risk Factors; Role; Sampling; Statistical Computing; Statistical Methods; Structure; Testing; Tissue-Specific Gene Expression; Tissues; base; biobank; causal variant; computerized tools; epidemiology study; flexibility; genetic variant; genome wide association study; insight; molecular imaging; multiple datasets; novel; pleiotropism; programs; protective factors; response; software development; statistics; theories; therapeutic development; trait; transcriptome; web site

Summary Alzheimer's disease (AD) affects over 44 million individuals worldwide, and the number is projected to triple by 2050. However, currently there is no cure for AD. Observational epidemiology studies have identified some modifiable lifestyle-related risk factors associated with AD; if these risk factors are indeed causal to, but not just effects of, AD, they can be targeted in interventions to reduce the incidence of AD. To alleviate the challenges facing observational studies with likely confounding and reverse causation, we develop and apply a suite of novel, robust and powerful causal inference methods by integrating the large amount of existing large-scale GWAS of AD and other traits. Specifically, first, going beyond existing two-sample Mendelian randomization (2SMR), we will develop the following new methods that are more powerful and more robust with less stringent modeling assumptions: transcriptome-wide association studies in the presence of confounding and invalid instrumental variables, co-localization detection of causal genetic variants for multiple traits, and orienting the causal direction between two traits using multiple (possibly correlated) genetic variants as instrumental variables. Second, we will adapt and apply both the new and existing methods to multiple large-scale GWAS datasets with AD and other molecular/imaging/clinical traits to comprehensively search and identify not only AD target genes, but also brain areas and their functional connectivities, and other risk factors, that are putatively causal to AD. As a byproduct, we will develop and distribute software implementing the proposed methods.

摘要 阿尔茨海默病(AD)影响着全球超过4400万人，预计这一数字将增加两倍 到2050年。然而，目前还没有治愈阿尔茨海默病的方法。观察流行病学研究已经证实了一些fi Modifi能够与AD相关的生活方式相关风险因素；如果这些风险因素确实是因果关系，但不仅仅是 对AD的影响，可以在干预中有针对性地降低AD的发生率。缓解挑战 面对可能存在混淆和反向因果关系的观察性研究，我们开发并应用了一套新颖的、 结合已有的大量大规模地理信息系统，提出了一种健壮、强大的因果推理方法 广告和其他特征。SpeifiCally，fiRst，超越了现有的两样本孟德尔随机化(2SMR)，我们 我将开发以下更强大、更健壮、建模不那么严格的新方法 假设：在存在混淆和无效工具的情况下进行转录组范围的关联研究 变量、多个性状的因果遗传变异的共定位检测以及因果方向的确定 使用多个(可能相关的)遗传变异作为工具变量的两个性状之间的差异。第二，我们将 将新的和现有的方法适应并应用于具有AD和其他数据的多个大规模GWAS数据集 分子/影像/临床特征不仅能全面搜索和识别AD靶基因，还能识别大脑 区域及其功能连接性，以及其他可能导致AD的危险因素。作为副产品， 我们将开发和分发实现所建议方法的软件。